Under-Review

This work designs and analyzes a novel set of algorithms for multi-agent reinforcement learning (MARL) based on the principle of information-directed sampling (IDS).

we propose a novel Vision-Language Preference learning framework that learns a vision-language preference model to provide preference feedback for embodied manipulation tasks.

We decompose the reward value in RLHF into two independent components that consists prompt-free reward and prompt-related reward, and propose a new reward learning algorithm by prioritizing data samples based on their prompt-free reward values.

We propose Adversarial Locomotion and Motion Imitation (ALMI) for humanoid robots, which serves as a novel framework for loco-manipulation tasks, enabling adversarial policy learning between upper and lower body.

We propose Diffusion-Inspired Multi-Agent world model (DIMA), a novel framework for multi-agent reinforcement learning that leverages diffusion models to reduce modeling complexity and improve sample efficiency.

We propose the Learn as Individuals, Evolve as a Team (LIET) framework to enable multi-agent LLMs to adapt to embodied environments through individual learning and team evolution

we propose HumanoidGen, an automated task creation and demonstration collection framework that leverages atomic dexterous operations and LLM reasoning to generate relational constraints.

We propose a novel bimanual foundation policy that leverages text-to-video models to predict robot trajectories and uses optical flow as an intermediate variable to improve generalization.

We propose a novel framework that enables humanoid robots to traverse complex terrains with controllable human-like gaits using a mixture of latent residual experts and multi-discriminators.

We propose a physics-based humanoid control framework, aiming to master highly-dynamic human behaviors such as Kungfu and dancing through multi-steps motion processing and adaptive motion tracking.